We

We Listened to the Data-Science Community - 3LC

Hacker News

We Listened to the Data-Science Community - 3LC

Hello HN, Based on community feedback, we listened and simplified the setup for 3LC. Now all you need to do is pip install 3lc, no account creation needed. What is 3LC? 3LC is an ML tool that gives detailed insights, real-time data-centric iterative workflows for training/finetuning, and data quality improvements for your Machine Learning datasets and models. 3LC serves as a visualizer, editor, and debugger, focusing on how models learn from the training data. It integrates with your existing training scripts and runs locally. Beta Now Live! And it's free. https://pypi.org/project/3lc/ docs here: https://docs.3lc.ai/3lc/latest/index.html overview + tutorial videos here: https://www.youtube.com/@3LCai

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, visualize · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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